Cellular Automata From First Principles 43: Test Generalization Beyond Training

Mon, 10 Aug 2026 20:39:00 +0100

Cellular Automata From First Principles 43: Test Generalization Beyond Training

A model can look robust while still depending on the exact conditions used during training.

So after growth, persistence and regeneration, we need a harder question:

what happens when the world changes?

Build a generalization matrix

Vary dimensions independently:

canvas size
seed position
update rate
rollout length
damage geometry
damage severity
noise level
boundary conditions

Then evaluate combinations that were not used during training.

Cellular Automata From First Principles 46: Generalize to Harder and Larger Mazes

Mon, 10 Aug 2026 20:39:00 +0100

Cellular Automata From First Principles 46: Generalize to Harder and Larger Mazes

Training accuracy answers the easiest question:

Can the model solve problems drawn from the training distribution?

The more interesting question is:

Did the local rule learn a reusable computation?

This chapter turns that into an explicit evaluation protocol.

Hold out dimensions of difficulty

Instead of one validation set, create several test families.

For example:

A: 32×32, familiar wall density
B: 48×48, familiar wall density
C: 64×64, familiar wall density
D: 32×32, denser obstacles
E: 32×32, longer required paths
F: different procedural maze generator
G: narrow bottlenecks and many dead ends

The point is not to maximize one aggregate score.

Generalization on Programmer.ie: Modern AI programming

Cellular Automata From First Principles 43: Test Generalization Beyond Training

Cellular Automata From First Principles 43: Test Generalization Beyond Training

Build a generalization matrix

Cellular Automata From First Principles 46: Generalize to Harder and Larger Mazes

Cellular Automata From First Principles 46: Generalize to Harder and Larger Mazes

Hold out dimensions of difficulty